Summary of Optimal Initialization Of Batch Bayesian Optimization, by Jiuge Ren and David Sweet
Optimal Initialization of Batch Bayesian Optimization
by Jiuge Ren, David Sweet
First submitted to arxiv on: 27 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning framework called Batch Bayesian Optimization (BBO) is designed for efficient experimentation in engineered systems. BBO combines Bayesian optimization with batched measurements, reducing the time required to evaluate system quality at different settings. A novel acquisition function, Minimal Terminal Variance (MTV), is proposed to optimize batch design, rather than relying on random sampling. MTV adapts an I-Optimality criterion from Design of Experiments to minimize variance and integrate over all possible settings. This framework can be used for both initial batch construction and subsequent batches, a novel feature among acquisition functions. Numerical experiments demonstrate the effectiveness of MTV compared to other BBO methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Batch Bayesian Optimization (BBO) is a way to quickly test different settings in engineered systems. It works by taking measurements of multiple settings at once, rather than one at a time. A new method called Minimal Terminal Variance (MTV) helps design these batches so they give the most information. MTV uses ideas from a field called Design of Experiments to decide which settings to measure first. This approach can be used for both starting and following batches, making it unique among BBO methods. |
Keywords
» Artificial intelligence » Machine learning » Optimization
